Identifying non-randomness in integrated circuit product yield

ABSTRACT

A method of analyzing integrated circuit (IC) product yield can include storing, within a memory of a system comprising a processor, parametric data from a manufacturing process of an IC and determining a measure of non-random variation for at least one parameter of the parametric data using a pattern detection technique. The processor can compare the measure of non-random variation to a randomness criteria and selectively output a notification indicating that variation in the parameter is non-random according to the comparison of the measure of non-random variation to the randomness criteria.

FIELD OF THE INVENTION

The embodiments disclosed within this specification relate to integratedcircuit (IC) devices. More particularly, the embodiments relate toimproving IC product yields.

BACKGROUND

A commercially successful integrated circuit (IC) is often dependentupon high product yields to keep manufacturing costs at a level thatprovides product profitability at market competitive prices. The phrase“product yield,” in terms of ICs, is typically defined as the percentageof functional ICs generated out of the total number of ICs fabricatedusing a particular manufacturing process for ICs. IC product yield isaffected by a variety of factors that permeate nearly the entiremanufacturing process.

One cause of decreased IC product yield relates to processimperfections. Process imperfections can create parameter variationsbetween process runs for an IC, between differing wafers within a singleprocess run, or between ICs within a same wafer. For example, the valueof a particular resistor can vary from one IC to another according tothe geographic region of the wafer in which each IC is located. Inanother example, capacitance values may be higher on wafers processed atthe beginning of a process run than wafers processed at the end of theprocess run.

Another cause of decreased IC product yield relates to the testingequipment itself. More particularly, test and/or measuring equipment canlose calibration or alignment resulting in the collection of inaccuratedata. Assessing inaccurate data can lead to the rejection of operableICs and, therefore, the needless degradation of product yields.

SUMMARY

The embodiments disclosed within this specification relate to integratedcircuits (ICs) and, more particularly, to improving IC product yields.One embodiment of the present invention includes a method of analyzingIC product yield within a system comprising a processor and a memory.The method can include storing, within the memory, parametric data froma manufacturing process for an IC and determining a measure ofnon-random variation for at least one parameter of the parametric datausing a pattern detection technique. The processor can compare themeasure of non-random variation to a randomness criterion andselectively output a notification indicating that variation in theparameter(s) is non-random according to the comparison of the measure ofnon-random variation to the randomness criteria.

Determining the measure of non-random variation can include determininga mean value for at least one parameter of the parametric data for eachof a plurality of wafers. Each wafer can include a plurality of the ICs.For each of a plurality of successive wafer pairs, a determination canbe made as to whether the mean value for a second wafer of thesuccessive wafer pair increases or decreases from the mean value of afirst wafer of the successive pair of wafers. The method can store anindication of whether the mean value increases or decreases for eachsuccessive wafer pair as a sequence of indications and calculate ameasure of non-random variation according to the sequence ofindications.

In one aspect, calculating the measure of non-random variation accordingto the sequence of indications can include determining a number ofoccurrences of a pattern of indications within the sequence ofindications and calculating the measure of non-random variationaccording to the number of occurrences of the pattern of indications.

Determining the measure of non-random variation can include, for each ofa plurality of data point sets, wherein each data point set comprises aplurality of data points for at least one parameter within theparametric data, calculating a curve fit for the data point set. Themeasure of non-random variation can be calculated according toY-intercept points of the curve fits. In one aspect, each data point setcan comprise a plurality of data points collected from a plurality ofICs associated with at least one site within a reticle field, whereinthe reticle field corresponds to a reticle used during the manufacturingprocess.

Determining the measure of non-random variation can include, for each ofa plurality of data point sets, calculating a curve fit for the datapoint set. Each data point set comprises a plurality of data points forat least one parameter of the parametric data. The measure of non-randomvariation can be calculated according to slopes of the curve fits. Inone aspect, each data point set can correspond to a plurality of datapoints collected from a plurality of ICs associated with at least onesite within a reticle field. The reticle field can correspond to areticle used during the manufacturing process.

In another aspect, determining a measure of non-random variation caninclude, for each of a plurality of IC device sets, determining a numberof bin failure occurrences within each IC device set. Each IC device setcan include at least one IC manufactured using the manufacturingprocess. A mean number of bin failure occurrences per IC can bedetermined. The measure of non-random variation can be calculatedaccording to the number of bin failure occurrences within the IC deviceset and the mean number of bin failure occurrences per IC device set.Each IC device set can include at least one IC associated with at leastone site of a test probe used during the manufacturing process.

Another embodiment of the present invention includes a system foranalyzing IC product yield that includes a memory storing program codeand parametric data from a manufacturing process of an IC. The systemfurther can include a processor coupled to the memory, wherein theprocessor, upon executing the program code, performs operationsincluding determining a measure of non-random variation for at least oneparameter of the parametric data using a pattern detection technique.The processor further can compare the measure of non-random variation toa randomness criterion and selectively output a notification indicatingthat variation in the parameter(s) is non-random according to thecomparison of the measure of non-random variation to the randomnesscriterion.

Determining the measure of non-random variation can include determininga mean value for at least one parameter of the parametric data for eachof a plurality of wafers. Each wafer can include a plurality of the ICs.For each of a plurality of successive wafer pairs, a determination canbe made as to whether the mean value for a second wafer of a successivewafer pair increases or decreases from the mean value of a first waferof the successive pair of wafers. Determining a measure of non-randomvariation further can include storing an indication of whether the meanvalue increases or decreases for each successive wafer pair as asequence of indications. A measure of non-random variation can becalculated according to the sequence of indications.

Calculating the measure of non-random variation according to thesequence of indications can include determining a number of occurrencesof a pattern of indications within the sequence of indications. Themeasure of non-random variation can be calculated according to thenumber of occurrences of the pattern of indications.

Determining the measure of non-random variation can include, for each ofa plurality of data point sets, calculating a curve fit for the datapoint set. Each data point set can include a plurality of data pointsfor a parameter of the parametric data. The measure of non-randomvariation can be calculated according to Y-intercept points of the curvefits.

Determining the measure of non-random variation can include, for each ofa plurality of data point sets, calculating a curve fit for the datapoint set. Each data point set can include a plurality of data pointsfor a parameter of the parametric data. The measure of non-randomvariation can be calculated according to slopes of the curve fits.

In another aspect, determining the measure of non-random variation caninclude, for each of a plurality of IC device sets, determining a numberof bin failure occurrences within each IC device set. Each IC device setcan include at least one IC manufactured using the manufacturingprocess. The system can determine a mean number of bin failureoccurrences per IC device set. The measure of non-random variation canbe calculated according to the number of bin failure occurrences withinthe IC device set and the mean number of bin failures occurrences per ICdevice set.

Another embodiment of the present invention can include an article ofmanufacture that includes a data storage device usable by a systemcomprising a processor and a memory. The data storage device storesprogram code that, when executed by the system, causes the system toperform executable operations. The executable operations can includestoring, within the memory, parametric data from a manufacturing processfor an IC and determining a measure of non-random variation for at leastone parameter of the parametric data using a pattern detectiontechnique. The executable operations can include comparing the measureof non-random variation to a randomness criterion. A notification can beselectively output indicating that variation in the parameter isnon-random according to the comparison of the measure of non-randomvariation to the randomness criterion.

The operation of determining a measure of non-random variation caninclude determining a mean value for at least one parameter of theparametric data for each of a plurality of wafers, wherein each wafercomprises a plurality of the ICs, and, for each of a plurality ofsuccessive wafer pairs, determining whether the mean value for a secondwafer of the successive wafer pair increases or decreases from the meanvalue of a first wafer of the successive pair of wafers. An indicationof whether the mean value increases or decreases for each successivewafer pair can be stored as a sequence of indications. A measure ofnon-random variation can be calculated according to the sequence ofindications.

The operation of determining a measure of non-random variation caninclude, for each of a plurality of data point sets, wherein each datapoint set comprises a plurality of data points for at least oneparameter within the parametric data, calculating a curve fit for thedata point set and calculating the measure of non-random variationaccording to Y-intercept points of the curve fits.

The operation of determining a measure of non-random variation caninclude, for each of a plurality of data point sets, wherein each datapoint set comprises a plurality of data points for at least oneparameter of the parametric data, calculating a curve fit for the datapoint set and calculating the measure of non-random variation accordingto slopes of the curve fits.

In another aspect, the operation of determining a measure of non-randomvariation can include, for each of a plurality of IC device sets,wherein each IC device set comprises at least one IC manufactured usingthe manufacturing process, determining a number of bin failureoccurrences within each IC device set and determining a mean number ofbin failure occurrences per IC device set. The measure of non-randomvariation can be calculated according to the number of bin failureoccurrences within the IC device sets and the mean number of bin failureoccurrences per IC device set.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system for improving yield of amanufacturing process for integrated circuits (ICs) in accordance withone embodiment of the present invention.

FIG. 2 is a first flow chart illustrating a method of improving yield ofa manufacturing process for an IC in accordance with another embodimentof the present invention.

FIG. 3 is a second flow chart illustrating a method of determining ameasure of non-random variation for a selected parameter of ICmanufacturing data in accordance with another embodiment of the presentinvention.

FIG. 4 is a first graph illustrating parametric data from an ICmanufacturing process in accordance with another embodiment of thepresent invention.

FIG. 5 is a third flow chart illustrating a method of determining ameasure of non-random variation for a selected parameter of ICmanufacturing data in accordance with another embodiment of the presentinvention.

FIG. 6 is a second graph illustrating parametric data from an ICmanufacturing process in accordance with another embodiment of thepresent invention.

FIG. 7 is a fourth flow chart illustrating a method of determining ameasure of non-random variation for a selected parameter of ICmanufacturing data in accordance with another embodiment of the presentinvention.

FIG. 8 is a third graph illustrating parametric data collected from ICmanufacturing process in accordance with another embodiment of thepresent invention.

DETAILED DESCRIPTION

While the specification concludes with claims defining the features ofthe embodiments of the invention that are regarded as novel, it isbelieved that the embodiments of the invention will be better understoodfrom a consideration of the description in conjunction with thedrawings. As required, detailed embodiments of the present invention aredisclosed herein; however, it is to be understood that the disclosedembodiments are merely exemplary of the inventive arrangements, whichcan be embodied in various forms. Therefore, specific structural andfunctional details disclosed herein are not to be interpreted aslimiting, but merely as a basis for the claims and as a representativebasis for teaching one skilled in the art to variously employ theinventive arrangements in virtually any appropriately detailedstructure. Further, the terms and phrases used herein are not intendedto be limiting but rather to provide an understandable description ofthe embodiments of the invention.

The embodiments disclosed within this specification relate to integratedcircuits (ICs), and more particularly to pattern mining IC relatedparametric data. In accordance with the inventive arrangements disclosedherein, parametric data from silicon wafers and IC test measurementscollected from, e.g., before, during, and/or after, an IC manufacturingprocess can be stored in memory. The stored parametric data can beanalyzed using one or more pattern detection techniques selected from apattern detection technique library. Using the pattern detectiontechnique(s), a measure of non-random variation for a selected parameterof the parametric data can be determined.

Increased levels of non-random variation in parametric data can indicatethe cause, or causes, of decreased IC product yield. Additionally,particular patterns of non-random variation in one or more parameters ofthe parametric data can be correlated with, and therefore indicate,known causes of IC product yield degradation. When the measure ofnon-random variation for the parameter meets one or more randomnesscriteria, a notification can be generated alerting a user to excessivelevels of non-random variation for the selected parameter(s) of theparametric data and, accordingly, to possible causes of reduced ICproduct yield. Detecting non-random data patterns within parametricdata, whether at the die, wafer, or tester level, can assist engineersin improving manufacturing processes, improving product performance, andpreventing catastrophic IC failures, thereby reducing IC manufacturingcosts and increasing IC reliability.

FIG. 1 is a block diagram illustrating a system 100 for improving yieldof a manufacturing process for an IC in accordance with one embodimentof the present invention. In one aspect, system 100 can analyzeparametric data collected during the manufacturing of an IC and analyzethe parametric data to identify likely causes of reduced IC productyield.

System 100 can include at least one processor 105 coupled to memoryelements 110 through a system bus 115. As such, system 100 can storeprogram code within memory elements 110. Processor 105 can execute theprogram code accessed from memory elements 110 via system bus 115. Inone aspect, for example, system 100 can be implemented as a computerthat is suitable for storing and/or executing program code such asanalysis engine 145. It should be appreciated, however, that system 100can be implemented in the form of any system comprising a processor andmemory that is capable of performing the functions described within thisspecification.

Memory elements 110 can include one or more physical memory devices suchas, for example, local memory 120 and one or more bulk storage devices125. Local memory 120 refers to random access memory or othernon-persistent memory device(s) generally used during actual executionof the program code. Bulk storage device(s) 125 can be implemented as ahard drive or other persistent data storage device. System 100 also caninclude one or more cache memories (not shown) that provide temporarystorage of at least some program code in order to reduce the number oftimes program code must be retrieved from bulk storage device 125 duringexecution.

Input/output (I/O) devices such as a keyboard 130, a display 135, and apointing device (not shown) optionally can be coupled to system 100. TheI/O devices can be coupled to system 100 either directly or throughintervening I/O controllers. Network adapters also can be coupled tosystem 100 to enable system 100 to become coupled to other systems,computer systems, remote printers, and/or remote storage devices throughintervening private or public networks. Modems, cable modems, andEthernet cards are examples of different types of network adapters thatcan be used with system 100.

In operation, analysis engine 145, executing within processor 105, canreceive and analyze parametric data 140 collected during an ICmanufacturing process. As such, parametric data 140 can include avariety of data types collected during fabrication of the IC, which caninclude data collected prior, and subsequent, to IC packaging.Parametric data 140 can include, but is not limited to, inline criticaldimension data, scribe e-test data, wafer probe data, temperaturedependent performance data, product sort data, i.e., both pre and postpackaging, and the like. Parametric data 140 can include measurements ofprocess and IC parameters such as process layer thickness and widthdimensions, unit resistances, unit capacitances, unit inductances,transistor transconductance, transistor switching speed, clock skew, orany other parameter that is measurable and describes a physical orperformance parameter of an IC or the manufacturing process used toimplement the IC. For example, parametric data 140 can be collected frommeasurements of standard test devices, occupying each wafer, which aremeasured to track changes in process parameters over successivemanufacturing runs of the IC.

To analyze parametric data 140, one or more pattern detection techniquescan be selected from pattern detection technique library (hereafter“library”) 165 to analyze one or more parameters of parametric data 140.Library 165 can include various pattern detection techniques that canindentify non-random variation of values for one or more selectedparameters within parametric data 140. The pattern detection techniquescan help identify IC fabrication and processing effects that can degradeIC product yield. These IC fabrication and processing effects caninclude, but are not limited to, wafer placement processing effects,test probe defects, reticle alignment issues, temperature and timerelated device degradation, processing trends, and the like. Eachpattern detection technique can be implemented within analysis engine145.

More particularly, each pattern detection technique from library 165 canspecify a particular type of effect, anomaly, or trait within parametricdata 140 to be analyzed. For example, one pattern detection techniquecan specify settings or instructions for one or more of the pairanalysis module 150, the curve fit analysis module 155, or thecumulative density module 160 for identifying non-randomness withrespect to odd even effects. Another pattern detection technique canspecify which ICs, e.g., every third or fourth, or which wafers, e.g.,every second, third, etc., are to be reviewed for non-random behavior.Each pattern detection technique will specify its own criteria orcriterion for non-randomness. As such, each pattern detection techniqueprovides a configuration to the relevant module of analysis engine 145to specify what phenomena are being search for within the parametricdata 140 and the criteria for determining whether the phenomena exhibitsnon-random behavior.

Analysis engine 145 can include a pair analysis module 150, a curve fitanalysis module 155, and a cumulative density module 160. To analyze aparameter of parametric data 140 using a selected pattern detectiontechnique from library 165, the pattern detection technique further canspecify, and the analysis module 145 can invoke, one of pair analysismodule 150, curve fit analysis module 155, or cumulative density module160. Each of pair analysis module 150, curve fit analysis module 155,and cumulative density module 160 can implement the processing specifiedby the selected pattern detection technique obtained from library 165 toanalyze parametric data 140.

When specified by a pattern detection technique of library 165, andinvoked by data analysis engine 145, pair analysis module 150 canreceive data for at least one parameter of parametric data 140 andanalyze the data for wafer to wafer non-random variation. In ICmanufacturing, multiple instances of an IC can be built within a singlesilicon wafer. Using a parameter of parametric data 140 collected fromeach IC within each of two or more wafers, pair analysis module 150 candetermine a mean value of the parameter for each wafer. Pair analysismodule 150 can calculate a measure of non-random variation for eachwafer according to the mean value of the parameter of the wafer.

When specified by a pattern detection technique of library 165, andinvoked by data analysis engine 145, curve fit analysis module 155 canreceive two or more data point sets. Each data point set can include twoor more data points for one or more parameters of parametric data 140.For each data point set, curve fit analysis module 150 can perform acurve fit using the data points within the data point set. UsingY-intercept and/or slope values of the curve fit for each data pointset, curve fit analysis module 155 can calculate a measure of non-randomvariation for each data point set.

When specified by a pattern detection technique of library 165, andinvoked by data analysis engine 145, cumulative density module 160 canreceive parametric data for two or more IC device sets. Each IC deviceset can include bin failure data for at least one IC manufactured usingan IC manufacturing process. Each IC device set represents a grouping ofICs according to a feature shared by each IC of the IC device set. Forexample, an IC device set can include each IC manufactured within aparticular wafer or a particular wafer lot. In another example, an ICdevice set can include each IC tested at a particular test site using amultisite probe card for a particular process run lot. Cumulativedensity module 160 can determine a number of bin failures occurringwithin each IC device set. In addition, cumulative density module 160can determine a mean number of bin failures occurring per IC device setamong a plurality of IC device sets. Using the number of bin failuresoccurring within each IC device set and the mean number of bin failuresoccurring per IC device set, cumulative density module 160 can calculatea measure of non-random variation for each IC device set.

The measure of non-random variation calculated by pair analysis module150, curve fit analysis module 155, or cumulative density module 160 canbe stored in memory elements 110. Analysis engine 145 can compare thestored measure of non-random variation to randomness criteria. As noted,the randomness criteria can be specified by the selected patterndetection technique. When the randomness criteria are met, notification170 can be output indicating that at least one parameter of theparametric data exhibits excessive levels of non-random variation. Asused herein, “outputting” and/or “output” can mean, for example, storingwithin memory elements 110, e.g., writing to a file stored within memoryelements 110, writing to the user display 135 or other output device,playing audible notifications, sending or transmitting to anothersystem, exporting, or the like.

FIG. 2 is a first flow chart illustrating a method 200 of improvingyield of a manufacturing process for an IC in accordance with anotherembodiment of the present invention. Method 200 can be implemented usinga system as described with reference to FIG. 1. In general, method 200describes a process of analyzing parametric data to detect non-randomvariation in selected items, or parameters, of the parametric data.Non-random variation in a parameter can be indicative of decreased ICproduct yield and further can indicate a cause of the decreased ICproduct yield.

Beginning in step 205, parametric data collected during a manufacturingprocess for an IC can be stored within memory of the system. Theparametric data can include measurement parameters collected for the ICduring wafer processing, wafer probe testing, package testing and sort,as well as any other data from the manufacturing process of the IC.

In step 210, the system can determine a measure of non-random variationfor a selected parameter of the parametric data using a selected datapattern detection technique. A plurality of pattern detectiontechniques, stored in a library, can be used to analyze the parametricdata. Each pattern detection technique can specify a unique method ofdetermining non-random variation with respect to one or more parametersof the parametric data.

In one embodiment, the parameter(s) of the parametric data to beanalyzed, and the pattern detection technique used to analyze theparameter(s), can be selectable by a user of the system. In anotherembodiment, the parameter(s) of the parametric data to be analyzed, andthe pattern detection technique used to analyze the parameter(s), can bedetermined by the system automatically.

In step 215, the determined measure of non-random variation for theparameter can be compared to a randomness criterion, or to randomnesscriteria, as the case may be. The randomness criteria that is used canvary according to the particular pattern detection technique applied tothe parametric data. More particularly, a different randomness criterioncan be used for each pattern detection technique. In one embodiment, therandomness criteria can be a threshold level for the measure ofnon-random variation that, when exceeded, signifies an excessive levelof non-random variation within the selected parameter(s) of theparametric data.

In decision box 220, the system can determine whether the measure ofnon-random variation for the parameter of the parametric data meets therandomness criterion. When the measure of non-random variation does notmeet the randomness criteria, e.g., the measure of non-random variationdoes not exceed an established threshold, method 200 can proceed todecision box 230. When the randomness criteria are met, e.g., themeasure of non-random variation does exceed a threshold, method 200 canproceed to step 225.

In step 225, the system can output a notification alerting a user of thesystem that the parametric data has met the randomness criteria, therebyexhibiting an unacceptable level of non-random variation. For example,the system can send an electronic mail or other electronic message,present a message upon a display, or provide another form ofnotification to a user. The notification can indicate the selectedpattern detection technique, the measure of non-random variation, andthe randomness criterion.

In decision box 230, when additional parameters are selected to beanalyzed or additional pattern detection techniques are selected to beapplied to the parameter or other parameters, method 200 can proceed tostep 235. In step 235, the next parameter to be analyzed, or next datapattern detection technique with which to analyze the same or otherparameter, can be selected. Method 200 can return to step 210 andcontinue analyzing the parametric data. When no further parameters ordata pattern detection techniques are to be applied or analyzed, method200 can end.

FIG. 3 is a second flow chart illustrating a method of determining ameasure of non-random variation for a selected parameter of ICmanufacturing data in accordance with another embodiment of the presentinvention. More particularly, the method of FIG. 3 illustrates oneembodiment of a method for determining a measure of non-random variationas described with reference to step 210 of FIG. 2.

Beginning in step 305, for each of a plurality of wafers, the system candetermine a mean value of a parameter of the parametric data collectedfor the ICs on each wafer. In step 310, the system can select a waferpair. The phrase “successive wafer pair” refers to two wafers that arepaired from a group of wafers. The wafers within the group from whichthe successive wafer pairs are created can be numbered one through N,where N is an integer representing a total number of wafers within thegroup. Successive wafer pairs can be created by pairing each set of twonumerically adjacent wafers together. For example, wafer one and wafertwo can form a first successive wafer pair. Wafer two and wafer threecan form the second successive wafer pair, etc.

The wafers of the group can be grouped according to a common feature ofthe wafers. For example, each wafer in the group can belong to a commonwafer process lot. As used within this specification, a “process runlot” or “wafer process lot,” can refer to one or more wafer(s)manufactured using a single manufacturing process run for an IC. Inaddition, the wafers can be numbered from one to N according to somecharacteristic of each wafer. For example, each wafer can be numberedaccording to the position of the wafer within the process run lot withone being the first wafer and N being the last wafer through themanufacturing process run. It should be appreciated that the waferswithin the group can be numbered according to any of a variety ofdifferent attributes or ranking systems and need not be numberedaccording to order of fabrication or order of creation, for example.

In step 315, the system can determine whether the mean value of a secondwafer of the selected successive wafer pair increases or decreases froma first wafer of the successive wafer pair. For example, a first waferof the selected successive wafer pair can have a mean value of 100 ohmsfor a particular resistor within the ICs of the first wafer. A secondwafer of the successive wafer pair can have a mean value of the resistorof 125 ohms within the ICs of the second wafer. Comparing the mean valueof the second wafer, i.e., 125 ohms, to the mean value of the firstwafer, i.e., 100 ohms, the system can determine that the successivewafer pair is an increasing successive wafer pair. When the mean valueof the second wafer is less than the mean value of the first wafer, thesuccessive wafer pair can be said to be a decreasing successive waferpair.

In step 320, the system can store in memory an indication of whether theselected successive wafer pair is an increasing or a decreasingsuccessive wafer pair. For example, six wafers can be formed into fivesuccessive wafer pairs. The system can compare the mean value of thefirst and second wafers within the first successive wafer pair anddetermine whether the first successive wafer pair is an increasing ordecreasing successive wafer pair. An increasing successive wafer paircan be stored as a one and a decreasing successive wafer pair can bestored as a zero, for example. Indications for each of the fivesuccessive wafer pairs can be stored as a sequence of zeros and ones,e.g. 11011.

In decision box 325, the system can determine whether the successivewafer pair analyzed is the last successive pair within the group ofwafers from which successive wafer pairs were formed. When the selectedsuccessive wafer pair is not the last successive wafer pair to beanalyzed, the method can loop back to step 310 to continue processing anext successive wafer pair. When the selected successive wafer pair isthe last successive wafer pair of the group of wafers, the method cancontinue to step 330.

In step 330, the system can determine a number of occurrences of apattern of indications within the sequence of indications for theplurality of wafers. Depending upon the particular pattern detectiontechnique selected, the system can determine a number of occurrences ofa particular pattern of indications, associated with that particularpattern detection technique, within the sequence of indications. Thegreater the number of occurrences of the particular pattern ofindications within the sequence of indications, the higher theprobability that non-random variation is present within the sequence ofindications and, accordingly, the parameter being analyzed.

Each pattern of indications can represent a type of non-random variationoccurring within a parameter of the parametric data for the plurality ofwafers. For example, a particular pattern detection technique can searchfor a pattern that indicates a parameter is increasing from wafer towafer during a process lot run. Typically, wafers within a same processwafer lot are assumed to have been manufactured under an identical, ornear identical, set of manufacturing process steps. When a sequence ofindications generated by the system for successive wafer pairs of aprocess wafer lot do not vary randomly between increasing indicationsand decreasing indications, non-random variation of a parameter isoccurring within the process wafer lot. For example, when eachindication within a sequence of indications for five successive waferpairs is an increasing indication, the variation within the sequence ofindications may be considered non-random since the wafers successivelyincrease from wafer to wafer within the plurality of wafers. Thisincrease in the mean value of a parameter from wafer to waferdemonstrates a trend as opposed to random variation.

In step 335, the system can calculate a measure of non-random variationfor the parameter of the parametric data collected for the plurality ofwafers according to the number of occurrences of the pattern ofindications within the sequence of indications. For example, a sequenceof indications generated for a mean wafer value of a parameter of aseries of successive wafer pairs is represented by a sequence of zerosand ones of 001001011. A selected data pattern detection techniqueinvokes the system to search for the occurrence of a pattern ofindications such as 001, for example, within the sequence ofindications. The system can determine two occurrences of the 001 patternof indications exist within the sequence of indications 001001011.

The system can calculate a measure of non-random variation for the meanvalue of the parameter within the plurality of wafers according to anumber of occurrences of the particular pattern of indications withinthe sequence of indications. Subsequent to calculating the measure ofnon-random variation, the method can continue to step 215 of method 200and compare the measure of non-random variation to a randomnesscriteria. For example, a calculated measure of non-random variation canbe calculated according to the two occurrences of the pattern ofindications 001. The measure of non-random variation, e.g., two, can bereturned to step 215 of FIG. 2. In one embodiment, the number ofoccurrences can be weighted by a factor, with the result being themeasure of non-random variation. In another embodiment, the system canattempt to detect more than one pattern, where different patterns can beweighted in terms of importance with the measure of non-random variationbeing a function of the number of times each of the different ones ofthe patterns are detected.

FIG. 4 is a first graph 400 illustrating parametric data from an ICmanufacturing process in accordance with another embodiment of thepresent invention. The parametric data presented within graph 400illustrates an embodiment in which parametric data is analyzed todetermine a measure of non-random variation for a process parameter ofthe parametric data collected from a manufacturing process for an ICthat is caused by a known IC or process effect. The level of non-randomvariation can be monitored to detect when the level of non-randomvariation for a parameter is significant enough to degrade IC productyields. Graph 400 can include a lot row 405, a wafer row 410, a data row415, a plurality of distribution box plots (box plots), and a data range425.

The parametric data presented within graph 400 can represent acollection of values obtained from an IC manufacturing process, or adevice testing protocol for an IC, for a parameter associated with theIC or a parameter associated with the manufacturing process for the IC.Lot row 405 within graph 400 identifies a particular process wafer lotto which a sub-set of the parametric data belongs. Wafer row 410 canidentify a particular wafer within each process wafer lot to which eachset of parametric values correspond. Data row 415 can contain aplurality of parametric values collected from particular wafers, whereinthe data collected from each individual wafer is shown as a box plot.Each box plot, such as box plot 420, encloses a range of parametricvalues collected from an individual wafer. Additionally, each box plotpresents a mean or median value for the parametric values collected foreach wafer. Data range 425 displays a set of quantitative values thatvertically align with, and identify the quantitative value of, theparametric values within each box plot as well as the value of the meanof the parametric data within each box plot.

For example, graph 400 can present resistance data for an IC. The datacan be collected by measuring the resistance of a test device, withineach IC, designed to be a 10 ohm resistor. In that case, box plot 420represents parametric data collected for each test device built withineach IC residing upon wafer nine of lot D. The vertical axis correspondsto, or represents, a data range 425, e.g., a resistance range that ismeasured in ohms. Data range 425 can be selected as one in which theexpected mean of the data being plotted is approximately centered andthe expected range for the data extends evenly above and below the mean.In the example pictured in FIG. 4, the expected mean can beapproximately 10 ohms, with the resistance range 425 being demarcated inunits of 1 ohm. Box plot 420 is positioned in data row 415 to align eachresistance value collected from wafer nine with a same quantitativevalue within data range 425 as measured for each IC device.

In one embodiment, a user may desire to examine the influence ofodd-even wafer effects upon the resistance data within graph 400.Odd-even effects result from differing physical positions of each waferwithin a wafer lot when moving through a manufacturing process run. Theuser can select odd-even effect from the library as the data patterndetection technique with which to analyze the resistance data in graph400. The system can determine the mean, the median, the standarddeviation, and the range for the collection of resistance values withineach box plot of graph 400.

As illustrated within graph 400 for wafer lot A of lot row 405, the boxplots associated with odd numbered wafers have mean values that trendlower than the means of box plots associated with even numbered wafers.This trend can indicate a shifting of the mean value of a resistorwithin an IC located on an odd numbered wafer from the mean value of aresistor within an IC located within an even numbered wafer. Thisshifting of the mean value of box plots can indicate non-randomvariation being introduced into the resistor values for the ICs byodd-even effects occurring during the manufacturing process. Excessivenon-random variation from odd-even effects can result in variation ofperformance parameters such as, for example, leakage current withinparticular wafers within wafer lot A.

A user can select odd-even effects from the library as a data patterndetection technique with which to determine a measure of non-randomvariation within a parameter of the parametric data of graph 400. Thesystem can compare the measure of non-random variation for the parameterto randomness criteria. The randomness criteria can specify a thresholdvalue for the measure of non-random variation that, when exceeded,indicates an excessive level of non-random variation from odd-eveneffects in the parameter of the parametric data. Measures of non-randomvariation for the parameter that exceed the threshold value can resultin reductions in IC product yield for an IC manufacturing process. Whenthe threshold value is exceeded, a notification can be generatedalerting the user to excessive non-random variation from odd-eveneffects within the parameter that may reduce IC product yields, e.g.,resistor variation excessive enough to result in inoperable ICs.

For example, referring to graph 400 in FIG. 4, tracking wafer mean plots430 and 435, greater variation between the mean values of box plots foreven numbered wafers and mean values of box plots for odd numberedwafers can be observed to occur within process lot D than occur withinprocess lot C. Wafer mean plots 430 and 435 illustrate the movement ofthe mean value of each box plot between successive wafers in each ofwafer lots C and D of graph 400. The slope of the line of wafer meanplots 430 and 435 between two successive box plots, whether positive ornegative, indicates whether the mean wafer value of a successive waferpair represented by the successive box plot is increasing or decreasing.Successive wafer pairs within wafer lot C and within wafer lot D can beanalyzed by the system for non-random variation of the mean wafer valueof the resistance for each of wafer lots C and D. Using wafer mean plots430 and 435 and assuming an increasing successive wafer pair isindicated by a 1 and a decreasing successive wafer pair is indicated bya 0, the system can generate a sequence of indications of 00010101 forlot C and a sequence of indications of 01010101 for lot D.

To calculate a measure of non-random variation within wafer lot C andwafer lot D, the system can determine a number of occurrences of apattern of indications, e.g., 01, within each of the sequence ofindications for wafer lots C and D. A repeating 01 pattern ofindications within a sequence of indications can reveal, for example,that the mean wafer value of each successive wafer in a wafer lot trendsin an opposite direction from a proceeding wafer in the wafer lot.Expressed in a different manner, the 01 pattern of indications indicatesthat odd numbered wafers within the wafer lot have a consistently highermean wafer value than even numbered wafers within the wafer lot. Thispattern of variation of the mean value of the resistance of each waferof a wafer lot is non-random, as opposed to a randomly dispersed seriesof 0's and 1's within a sequence of indications.

Continuing, the system can determine that 3 occurrences of the patternof indications 01 exist in the sequence of indications for lot C and 4occurrences of the pattern of indications 01 exist in the sequence ofindications for lot D. To calculate a measure of non-random variation,the equation M_(NV)=(N_(OCC)*N_(P))/N_(SEQ) can be applied to theparametric data of wafer lots C and D. In this equation, M_(NV)represents the measure of non-random variation, N_(OCC) represents anumber of occurrences of a pattern of indications within a sequence ofindications, N_(P) represents a number of indications in the pattern ofindications, and N_(SEQ) represents a number of indications in asequence of indications. Implementing this equation for the sequence ofindications generated for lots C and D, returns a measure of non-randomvariation of 0.75 for lots C, i.e., 3*2/8=0.75, and a measure ofnon-random variation of 1.00 for lot D, i.e., 4*2/8=1.00. As the measureof non-random variation for lot D is greater than lot C, the mean wafervalue of the resistance exhibits greater levels of non-random variationassociated with odd-even effects in wafer lot D than exhibited in waferlot C.

The measures of non-random variation for wafer lots C and D can becompared to randomness criteria. In one embodiment, the system canoutput a notification when the measure of non-random variation of awafer lot exceeds the randomness criteria. For example, the randomnesscriteria can be a threshold level of 0.8. When the measure of non-randomvariation exceeds 0.8, the system can output a notification pertainingto the relevant wafer lot. In that case, a notification, e.g., a coloredhighlighting of the measure of non-randomness variation for theparameter, can be output for wafer lot D since the measure of non-randomvariation for wafer lot D is greater than 0.8. No notification would begenerated for wafer lot C since the measure of non-random variation doesnot exceed 0.8.

It should be appreciated that while specified predetermined bit patternsare provided as examples of bit patterns for which the system can searchwithin an indication sequence, the system is not limited in this regard.The system can apply any of a variety of different statistical and/orstochastic techniques to determine whether a given sequence ofindications is random or non-random. For example, various compressiontechniques can be applied to the sequence of indications to determine ameasure of non-randomness that is correlated with the amount ofcompression achieved.

FIG. 5 is a third flow chart illustrating a method of determining ameasure of non-random variation for a selected parameter of ICmanufacturing data in accordance with another embodiment of the presentinvention. More particularly, the method of FIG. 5 illustrates anotherembodiment of a method for determining a measure of non-random variationas described with reference to step 210 of FIG. 2.

Beginning in step 505, the system can calculate a curve fit for each ofa plurality of data point sets representing parametric data related tothe manufacturing of an IC. Each data point set can include two or moredata points grouped according to a common feature. For example, eachdata point for a data point set can represent parametric data collectedfrom an individual wafer. Each data point represents a first parameterof the parametric data plotted as a function of a second parameter ofthe parametric data. For example, a data point can represent a maximummeasured output frequency of, and, inversely, the time delay, through afeedback path within a ring oscillator residing within an IC that isplotted as a function of total leakage current measured through the IC.The leakage current can be denoted as I_(LK). The curve fittingperformed by the system can include, but is not limited to, linear curvefitting, polynomial curve fitting, cubic fitting, geometric curvefitting, exponential curve fitting, or the like.

In step 510, the system can determine a Y-intercept point and a slopefor each curve fit calculated for a data point set. In step 515, thesystem can calculate Y-intercept ratios and slope ratios across datapoint sets. More particularly, for each Y-intercept (and thus data pointset), a ratio can be calculated with the selected Y-intercept to eachother Y-intercept. For example, curve fitting can be performed for eachof 3 data point sets with a Y-intercept point being determined for eachcurve fit. The Y-intercept points of the three data point sets, referredto as data set one, data set two, and data set three, can be representedas A₁, A₂, and A₃, respectively. The Y-intercept ratios for data pointset one can be defined as A₁/A₂, and A₁/A₃. The Y-intercept ratios fordata point set two can be defined as A₂/A₁, and A₂/A₃. The Y-interceptratios for data point set three can be defined as A₃/A₁, and A₃/A₂.

A similar procedure can be performed with respect to slopes of thedifferent curve fits of the data point sets. For example, curve fittingcan be performed for each of 3 data point sets and a slope determinedfor each curve fit. The slope of the three curve fits, in reference todata set one, data set two, and data set three, can be represented asB₁, B₂, and B₃, respectively. The slope ratios for data point set onecan be defined as B₁/B₂, and B₁/B₃. The slope ratios for data point settwo can be defined as B₂/B₁, and B₂/B₃. The slope ratios for data pointset three can be defined as B₃/B₁, and B₃/B₂.

In step 520, the system can determine a measure of non-random variationfor each data point set according to the Y-intercept and slope ratioscalculated for each data point set. The method can continue to step 215of method 200 and compare the measure of non-random variation of eachdata point set to randomness criteria. For example, increasing anddecreasing Y-intercept and/or slope ratio pairs can be determined toderive a sequence of indications as described. The sequence ofindications can be processed to derive a measure of non-randomvariation.

FIG. 6 is a second graph 600 illustrating parametric data collectedduring an IC manufacturing process in accordance with another embodimentof the present invention. The parametric data presented within graph 600illustrates one embodiment of the method described with reference toFIG. 5, wherein each data point set of the parametric data is analyzedto determine a measure of non-random variation within the data pointset, caused by a known IC or process effect. Graph 600 can include anX-axis 610, a Y-axis 615, data point set 620, data point set 625, linearcurve fit 630, and linear curve fit 635.

Graph 600 illustrates two data point sets, i.e., data point sets 620 and625, each being comprised of a plurality of data points representingparametric data from a manufacturing process for an IC. Each data pointrepresents a first parameter of the parametric data plotted as afunction of a second parameter of the parametric data. For example, adata set can include a plurality of data points, each data pointrepresenting a measured leakage current, denoted as I_(LK), within an ICversus a measured value of a signal path time delay through a transistordevice implemented within the IC. In that case, X-axis 610, being ameasure of current, can be expressed in milliamps. Y-axis 615, being ameasure time delay, can be expressed in nanoseconds.

Typically, the first parameter and the second parameter of a data pointare correlated in some manner. Plotting data points as presented ingraph 600 can visually illustrate the effect of the second parameterupon the first parameter. For example, an increase in I_(LK) within atransistor typically negatively affects the speed of, and time delaythrough, the transistor. Plotting data points representing the timedelay through the transistor as a function of I_(LK) within thetransistor can visually demonstrate the relationship between I_(LK) andtransistor speed.

As illustrated in FIG. 6, each of data point sets 620 and 625 canrepresent parametric data collected for two sites within a reticle fieldused to manufacture an IC. Data point set 620 is collected from ICsmanufactured at die sites one and four of reticle map 605. Data pointset 625 is collected from ICs manufactured at die sites two and three ofreticle map 605. A “reticle” refers to an etched glass surface throughwhich patterns on a semiconductor material can be optically defined.Each reticle defines a pattern on the semiconductor material associatedwith a process layer in the manufacturing process for an IC device. Asdie sites one and four of reticle map 605 are at opposing corners anddie sites two and three of reticle map 605 are at opposing corners,comparing data point set 620 to data point set 625 can reveal non-randomvariation within the parametric data that may be associated with causesof variation between the four reticle fields. For example, thisvariation can result from misalignment of a reticle during manufacturingof an IC with the reticle.

A linear curve fit can be applied by the system to each of data pointsets 620 and 625. Linear curve fit 630 represents a linear curve fit fordata point set 620. Linear curve fit 635 represents a linear curve fitfor data point set 625. Each of linear curve fits 630 and 635 can berepresented with an equation Y=A*X+B, with A being the slope and B beingthe Y-intercept point of the linear curve fit. A ratio can be calculatedbetween the Y-intercept point of linear curve fit 630 and theY-intercept point of linear curve fit 635. Additionally, a ratio can becalculated between the slope of linear curve fit 630 and the slope oflinear curve fit 635. For example, linear curve fits 630 and 635 can berepresented by equations Y=A₁*X+B₁ and Y=A₂*X+B₂, respectively. Ameasure of non-random variation for data point sets 620 and 625 can becalculated using the ratios A₁/A₂ and A₂/A₁, respectively. In a similarmanner, a measure of non-random variation for data point sets 620 and625 can be calculated using the ratios B₁/B₂ and B₂/B₁, respectively. Inone embodiment, three or more data point sets may be used. In that case,multiple ratios may be calculated for each data point set between theslope of the data point set and the slope of each other data point set.Similarly, multiple ratios may be calculated for each data point setbetween the Y-intercept point of the data point set and the Y-interceptpoint of each other data point set.

The measures of non-random variation for each of data point sets 620 and625 can be compared to randomness criteria. In one embodiment, thesystem can output a notification, e.g., a notification via email orinstant messaging, when the measure of non-random variation of eitherdata point sets 620 and/or 625 exceeds the randomness criteria. Asnoted, ratios of the Y-intercepts and/or slopes can be compared withthresholds defined as the randomness criteria. It should be appreciatedthat Y-intercept ratios can be evaluated independently of slope ratiosor can be evaluated in combination, e.g., where the ratio of Y-interceptand slope of given data point sets can be compared with a threshold.Emerging patterns of the ratios also can be determined, with occurrencesof such patterns being used as the measure of non-randomness to becompared against the randomness criteria.

FIG. 7 is a fourth flow chart illustrating a method of determining ameasure of non-random variation for a selected parameter of ICmanufacturing data in accordance with another embodiment of the presentinvention. More particularly, the method of FIG. 7 illustrates anotherembodiment of a method for determining a measure of non-random variationas described with reference to step 210 of FIG. 2.

Beginning in step 705, the system can determine a number of bin failuresoccurring within each of a plurality of IC device sets for a particularbin failure type. Each IC device set includes two or more ICs groupedaccording to a common feature. For example, each of a plurality of ICswithin an IC device set can be manufactured within a single siliconwafer. Each bin failure represents an IC device failure occurring withinan IC device set. A used within this specification, a “bin failure,”refers to a determination that an IC device is inadequate for sale oruse as a result of a particular inadequacy in the manufacturing processor the performance of the IC. Each bin failure type designates aparticular inadequacy responsible for the failure of the IC, i.e., a binfailure type.

In step 710, the system can determine a mean number of bin failuresoccurring per IC device set, for the plurality of IC device sets, for aparticular bin failure type. For example, for a particular bin type, theplurality of IC device sets can include an IC device set A in whichthree bin failures occur, an IC device set B in which two bin failuresoccur, and an IC devices set C in which four bin failures occur. In thatcase, the mean number of bin failures occurring per IC device set for ICdevice sets A, B, and C is three, i.e., (3+2+4)/3=3.

In step 715, where each of the plurality of IC device sets is comprisedof a plurality IC device sub-sets, the system can optionally determine amean number of bin failures, for the particular bin failure type,occurring per IC device sub-set for each of the plurality of IC devicesub-sets within each IC device set. In one embodiment, each of theplurality of IC device sets can be comprised of a plurality of sub-setsof IC devices, which, when combined, form each IC device set. Forexample, IC device set A can be comprised of IC device sub-sets A₁, A₂,and A₃. IC device sub-sets A₁, A₂, and A₃ collectively form IC deviceset A. A mean number of bin failures per IC device sub-set for each ofthe plurality of IC device sub-sets can be determined by summing thenumber of bin failures occurring within each of IC device sub-sets A₁,A₂, and A₃, and dividing the sum by the total number of IC devicesub-sets in IC device set A. For example, IC devices sub-sets A₁, A₂,and A₃ can have one, two, and three bin failures, respectively. In thatcase, the mean number of bin failures occurring per IC device sub-setwithin IC device set A is two, i.e., 1+2+3/3=2.

In step 720, the system can calculate a measure of non-random variationfor each IC device set according to a number of bin failures occurringwithin the IC device set, the mean number of bin failures occurring perIC device set, and, optionally, the mean number of bin failuresoccurring per IC device sub-set for the plurality of IC device sub-setswithin the IC device set. The method 700 can then return to step 215 ofmethod 200 and compare the measure of non-random variation for each ICdevice set to a randomness criteria.

FIG. 8 is a third graph 800 illustrating parametric data from an ICmanufacturing process in accordance with another embodiment of thepresent invention. The parametric data within graph 800 illustrates anembodiment in which an IC device set is analyzed to determine a measureof non-random variation, caused by a known IC or process effectoccurring within the IC device set. Graph 800 can include a plurality ofrows labeled as probe site number row (site number row) 805, lot row810, wafer row 815, and bin failures row 820.

The parametric data presented within graph 800 can represent parametricdata collected via probe card testing during the manufacturing of an ICdevice. The parametric data presents a number of bin failures of ICdevices, identified through probe card testing and identified as aparticular bin failure type, occurring during the manufacturing process.In conventional probe card testing of IC devices, whether at wafer leveltesting or at packaged IC level testing, a multi-sited test probe cardcan be used that tests performance parameters of multiple ICssimultaneously. Site number row 805 identifies the probe site within thetest probe card at which a bin failure occurred for an IC. Asillustrated within graph 800, site number row 805 identifies a probesite of the test probe card labeled as sites zero, one, two, and three,wherein each has been associated with a particular bin failure.

Lot row 810 identifies a wafer lot run in which an IC associated with aparticular bin failure was manufactured. Wafer row 815 identifies aparticular wafer within a wafer lot run in which the IC associated withthe particular bin failure was manufactured. Bin failure row 820illustrates a quantity of bin failures occurring within a particularwafer of a particular wafer lot run tested at a particular probe cardsite. The quantitative number of bin failures, for a particular binfailure type, occurring within a particular wafer of a particular waferlot run tested at a particular probe card site is identified graphicallyby the indicators, e.g., indicator 825, located directly above the waferassociated with the quantity of bin failures in bin failures row 820.

For example, only one instance of four bin failures within one waferoccurs within graph 800, at indicator 825. Aligned directly beneathindicator 825, within wafer row 815, wafer seven is the wafer identifiedas the wafer associated with the four bin failures. Directly beneathwafer seven and indicator 825, in lot row 810, lot C is the processwafer lot associated with the four bin failures. Directly below lot Cand indicator 825, in site number row 805, probe card site zero is theprobe card site associated with the four bin failures. Accordingly, theonly instance of four bin failures occurring within the parametric dataof graph 800 was collected at probe card site zero, within wafer seven,of wafer lot C.

In one embodiment, the system can determine a measure of non-randomvariation within an IC device set. The IC device set can include all ICsmanufactured within a same silicon wafer. The system can determine anumber of bin failures occurring within each wafer for a particular binfailure type. Each of the plurality of IC device sets can include eachwafer manufactured with a single wafer lot. The system can determine themean number of bin failures occurring per wafer within the wafer lot forthe particular bin failure type. Using the number of bin failures foreach wafer and the mean number of bin failures occurring per waferwithin the wafer lot, the system can determine a measure of non-randomvariation for each wafer manufactured within the process wafer lot.

For example, referring to FIG. 8, the system can determine a number ofbin failures occurring within each wafer of wafer lot C collected atprobe site zero for a particular bin failure type. The system candetermine a mean number of bin failures occurring per wafer, for theparticular bin failure type, within wafer lot C as the total number ofbin failures occurring within wafer lot C divided by the total number ofwafers within wafer lot C, i.e., 19/10=1.9. The system can calculate ameasure of non-random variation within each wafer using the equation:M_(NV)=(N_(W)−N_(AVE))/N_(AVE). In this equation, M_(NV) represents themeasure of non-random variation, N_(W) represents a number of binfailures occurring within a wafer and N_(AVE) represents an averagenumber of bin failures occurring per wafer. Using this equation, themeasure of non-random variation within wafer four is (3−1.9)/1.9, or0.58. The measure of non-random variation for wafer seven is(4−1.9)/1.9, or 1.10. The measure of non-random variation for wafer nineis (1−1.9)/1.9, or −0.47. A value of zero for M_(NV) represents a lackof non-random variation in a wafer. Accordingly, as wafer nine has ameasure of non-random variation nearest to zero, i.e., −0.47, wafer nineexhibits the lowest level of non-random variation of the three wafers.Wafer seven, having the measure of non-random variation furthest fromzero, i.e., 1.1, exhibits the highest level of non-random variation ofthe three wafers.

In another embodiment, each of the plurality of IC device sets caninclude at least one IC from at least one wafer of at least one waferprocess lot tested at a particular test probe site. The system candetermine a number of bin failures occurring among ICs tested at eachtest probe site for a particular bin failure type. The system,optionally, can determine a mean number of bin failures occurring perwafer among the wafers collected at each probe test site for theparticular bin failure type. Using the mean number of bin failuresoccurring per wafer for each test probe site, the system can calculate ameasure of non-random variation for each test probe site.

For example, referring to FIG. 8, the system can determine a mean numberof bin failures per wafer for each of probe test sites zero throughthree for a particular bin failure type. The mean number of bin failuresoccurring, for the particular bin failure type, per wafer for each probetest site can be determined by dividing the total number of bin failuresoccurring at each test probe site by the total number of wafers testedat the test probe site. For probe site zero, the mean number of binfailures occurring per wafer equals[(12*1)+(6*2)+(2*3)+(1*4)]/(4+7+9+2), or 1.55 bin failures occurring perwafer. Within this expression, referring to the numerator, the numbers“12,” “6,” “2,” and “1” of each factor pair indicate the number ofwafers with one, two, three, and four bin failure(s) per wafer,respectively, tested at probe site zero. Within the denominator, thenumbers “4,” “7,” “9,” and “2” represent the number of wafers in lots A,B, C, and D, respectively, tested at probe site zero.

At each of test probe sites one, two and three, the mean number of binfailures occurring per wafer equals one as each wafer at test probesites one through three has exactly one bin failure. Accordingly, testprobe site zero exhibits greater non-random variation in the number ofbin failures occurring per wafer than test probe sites one throughthree. The increased level of non-random variation in test probe sitezero may indicate a problem with the operation of test probe site zerowhen testing IC devices. As a result, IC devices tested at test probesite zero may be classified as bin failures that are actually fullyfunctional IC devices.

The embodiments disclosed within this specification can be implementedto monitor IC product yield for a variety of different types of ICs,whether custom ICs, application specific integrated circuits (ASICs),mixed signal ICs, or programmable ICs. Programmable ICs are a type of ICthat can be programmed to perform specified logic functions.

The flowcharts in the figures illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present invention. In this regard, each block in the flowchartsmay represent a module, segment, or portion of code, which comprises oneor more portions of executable program code that implements thespecified logical function(s).

It should be noted that, in some alternative implementations, thefunctions noted in the blocks may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It also should be noted that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and executable instructions.

Embodiments of the present invention can be realized in hardware or acombination of hardware and software. The embodiments can be realized ina centralized fashion in one system or in a distributed fashion wheredifferent elements are spread across several interconnected systems. Anykind of data processing system or other apparatus adapted for carryingout the methods described herein is suited.

Embodiments of the present invention further can be embedded in a devicesuch as a computer program product, which comprises all the featuresenabling the implementation of the methods described herein. The devicecan include a data storage medium or device, e.g., a computer-usable orcomputer-readable device, storing program code that, when loaded andexecuted in a system comprising memory and a processor, causes thesystem to perform the operations described herein. Examples of datastorage devices can include, but are not limited to, optical devices,magnetic devices, magneto-optical devices, computer memory such asrandom access memory or hard disk(s), or the like.

The terms “computer program,” “software,” “application,”“computer-usable program code,” “program code,” “executable code,”variants and/or combinations thereof, in the present context, mean anyexpression, in any language, code or notation, of a set of instructionsintended to cause a system having an information processing capabilityto perform a particular function either directly or after either or bothof the following: a) conversion to another language, code or notation;b) reproduction in a different material form. For example, program codecan include, but is not limited to, a subroutine, a function, aprocedure, an object method, an object implementation, an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system.

The terms “a” and “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising, i.e., open language. The term “coupled,” asused herein, is defined as connected, whether directly without anyintervening elements or indirectly with one or more interveningelements, unless otherwise indicated. Two elements also can be coupledmechanically, electrically, or communicatively linked through acommunication channel, pathway, network, or system.

The embodiments disclosed herein can be embodied in other forms withoutdeparting from the spirit or essential attributes thereof. Accordingly,reference should be made to the following claims, rather than to theforegoing specification, as indicating the scope of the embodiments ofthe present invention.

1. Within a system comprising a processor and a memory, a method ofanalyzing integrated circuit (IC) product yield, the method comprising:storing, within the memory, parametric data from a manufacturing processfor an IC; determining a measure of non-random variation for at leastone parameter of the parametric data using a pattern detectiontechnique; comparing, by the processor, the measure of non-randomvariation to a randomness criterion; and selectively outputting anotification indicating that variation in the at least one parameter isnon-random according to the comparison of the measure of non-randomvariation to the randomness criterion; wherein determining a measure ofnon-random variation further comprises: determining a mean value for atleast one parameter of the parametric data for each of a plurality ofwafers, wherein each wafer comprises a plurality of the ICs; for each ofa plurality of successive wafer pairs, determining whether the meanvalue for a second wafer of the successive wafer pair increases ordecreases from the mean value of a first wafer of the successive pair ofwafers; storing an indication of whether the mean value increases ordecreases for each successive wafer pair as a sequence of indications;and calculating a measure of non-random variation according to thesequence of indications.
 2. The method of claim 1, wherein calculating ameasure of non-random variation according to the sequence of indicationsfurther comprises: determining a number of occurrences of a pattern ofindications within the sequence of indications; and calculating themeasure of non-random variation according to the number of occurrencesof the pattern of indications.
 3. Within a system comprising a processorand a memory, a method of analyzing integrated circuit (IC) productyield, the method comprising: storing, within the memory, parametricdata from a manufacturing process for an IC; determining a measure ofnon-random variation for at least one parameter of the parametric datausing a pattern detection technique; comparing, by the processor, themeasure of non-random variation to a randomness criterion; andselectively outputting a notification indicating that variation in theat least one parameter is non-random according to the comparison of themeasure of non-random variation to the randomness criterion; whereindetermining a measure of non-random variation comprises: for each of aplurality of data point sets, wherein each data point set comprises aplurality of data points for at least one parameter within theparametric data, calculating a curve fit for the data point set; andcalculating the measure of non-random variation according to ratios ofY-intercept points of the curve fits.
 4. The method of claim 3, wherein:each data point set comprises a plurality of data points collected froma plurality of the ICs associated with at least one site within areticle field; and the reticle field corresponds to a reticle usedduring the manufacturing process.
 5. Within a system comprising aprocessor and a memory, a method of analyzing integrated circuit (IC)product yield, the method comprising: storing, within the memory,parametric data from a manufacturing process for an IC; determining ameasure of non-random variation for at least one parameter of theparametric data using a pattern detection technique; comparing, by theprocessor, the measure of non-random variation to a randomnesscriterion; and selectively outputting a notification indicating thatvariation in the at least one parameter is non-random according to thecomparison of the measure of non-random variation to the randomnesscriterion; wherein determining a measure of non-random variationcomprises: for each of a plurality of data point sets, wherein each datapoint set comprises a plurality of data points for at least oneparameter of the parametric data, calculating a curve fit for the datapoint set; and calculating the measure of non-random variation accordingto ratios of slopes of the curve fits.
 6. The method of claim 5,wherein: each data point set corresponds to a plurality of data pointscollected from a plurality of the ICs associated with at least one sitewithin a reticle field; and the reticle field corresponds to a reticleused during the manufacturing process.
 7. Within a system comprising aprocessor and a memory, a method of analyzing integrated circuit (IC)product yield, the method comprising: storing, within the memory,parametric data from a manufacturing process for an IC; determining ameasure of non-random variation for at least one parameter of theparametric data using a pattern detection technique; comparing, by theprocessor, the measure of non-random variation to a randomnesscriterion; and selectively outputting a notification indicating thatvariation in the at least one parameter is non-random according to thecomparison of the measure of non-random variation to the randomnesscriterion; wherein determining a measure of non-random variationcomprises: for each of a plurality of IC device sets, wherein each ICdevice set comprises at least one IC manufactured using themanufacturing process, determining a number of bin failure occurrenceswithin each IC device set; determining a mean number of bin failureoccurrences per IC device set; and calculating the measure of non-randomvariation according to the number of bin failure occurrences within theIC device sets and the mean number of bin failure occurrences per ICdevice set.
 8. The method of claim 1, wherein the measure of non-randomvariation is calculated from parametric data for at least two ICs. 9.The method of claim 3, wherein the measure of non-random variation iscalculated from parametric data for at least two ICs.
 10. The method ofclaim 5, wherein the measure of non-random variation is calculated fromparametric data for at least two ICs.
 11. The method of claim 7, whereinthe measure of non-random variation is calculated from parametric datafor at least two ICs.